Automated semiconductor wafer defect classification dealing with imbalanced data

Po-Hsuan Lee; Zhe Wang; Cho Teh; Yi-Sing Hsiao; Wei Fang

doi:10.1117/12.2552838

20 March 2020 Automated semiconductor wafer defect classification dealing with imbalanced data

Po-Hsuan Lee, Zhe Wang, Cho Teh, Yi-Sing Hsiao, Wei Fang

Author Affiliations +

Proceedings Volume 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV; 1132526 (2020) https://doi.org/10.1117/12.2552838
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

In the semiconductor manufacturing industry, Automatic Defect Classification (ADC) plays an important role in maintaining high wafer inspection quality and reducing yield loss. ADC performance has benefitted from using machine learning (ML) algorithms; however, performance is negatively affected by the data imbalance and limited amounts of training data. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique to adjust the skewed class distribution of a dataset so that the bias of the majority class is reduced. This paper shows that applying SMOTE achieved higher accuracy and purity on two imbalanced datasets, consisting of scanning electron microscopy (SEM) images collected with ASML-HMI eP™ and eScan^® series inspection tools. The ML models are also less sensitive to the selection of hyperparameters when SMOTE is applied. We also show that better classification results can be obtained with less training samples with SMOTE; we conducted an experiment where a ML model trained on only 25% of samples with SMOTE achieved a higher ADC accuracy and purity performance compared to the same ML model trained on all samples but without SMOTE. In another experiment using a highly imbalanced SEM dataset with very few counts of the defect-of- interest (DOI), the combination of SMOTE and random undersampling of the majority class improves the accuracy by up to 5x while maintaining the same level of purity.

Citation Download Citation

Po-Hsuan Lee, Zhe Wang, Cho Teh, Yi-Sing Hsiao, and Wei Fang "Automated semiconductor wafer defect classification dealing with imbalanced data", Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 1132526 (20 March 2020); https://doi.org/10.1117/12.2552838

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